Artificial neural networks (ANNs), inspired by the interconnection of real
neurons, have achieved unprecedented success in various fields such as computer
vision and natural language processing. Recently, a novel mathematical ANN
model, known as the dendritic neuron model (DNM), has been proposed to address
nonlinear problems by more accurately reflecting the structure of real neurons.
However, the single-output design limits its capability to handle multi-output
tasks, significantly lowering its applications. In this paper, we propose a
novel multi-in and multi-out dendritic neuron model (MODN) to tackle
multi-output tasks. Our core idea is to introduce a filtering matrix to the
soma layer to adaptively select the desired dendrites to regress each output.
Because such a matrix is designed to be learnable, MODN can explore the
relationship between each dendrite and output to provide a better solution to
downstream tasks. We also model a telodendron layer into MODN to simulate
better the real neuron behavior. Importantly, MODN is a more general and
unified framework that can be naturally specialized as the DNM by customizing
the filtering matrix. To explore the optimization of MODN, we investigate both
heuristic and gradient-based optimizers and introduce a 2-step training method
for MODN. Extensive experimental results performed on 11 datasets on both
binary and multi-class classification tasks demonstrate the effectiveness of
MODN, with respect to accuracy, convergence, and generality